Package: lite 1.1.1

lite: Likelihood-Based Inference for Time Series Extremes

Performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) <doi:10.1002/env.2133>. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.

Authors:Paul J. Northrop [aut, cre, cph]

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Bug tracker:https://github.com/paulnorthrop/lite/issues

On CRAN:

clusteredextremal-indexextreme-value-statisticsextremesfrequentistgeneralised-paretoinferencelikelihoodlog-likelihoodthresholdtime-series

7 exports 3 stars 1.02 score 54 dependencies 12 scripts 280 downloads

Last updated 2 months agofrom:1efc46871f. Checks:OK: 7. Indexed: yes.

Exports:blitefitBernoullifitGPflitegpObsInfologLikVectorreturnLevel

Dependencies:abindbackportsbayesplotchandwichcheckmateclicolorspacedistributionaldplyrexdexfansifarvergenericsggplot2ggridgesgluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgconfigplyrposteriorR6RColorBrewerRcppRcppArmadilloRcppRollreshape2revdbayesrlangrustsandwichscalesstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithrzoo